CN112693521A - Hydrostatic drive tracked vehicle neural network PID steering control method - Google Patents

Hydrostatic drive tracked vehicle neural network PID steering control method Download PDF

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Publication number
CN112693521A
CN112693521A CN202110023780.1A CN202110023780A CN112693521A CN 112693521 A CN112693521 A CN 112693521A CN 202110023780 A CN202110023780 A CN 202110023780A CN 112693521 A CN112693521 A CN 112693521A
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neural network
vehicle
control method
steering
steering control
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Inventor
陈漫
郑长松
于亮
黄静秋
伍懿
杜金波
刘新
周围
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Beijing Institute of Technology BIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D11/00—Steering non-deflectable wheels; Steering endless tracks or the like
    • B62D11/02—Steering non-deflectable wheels; Steering endless tracks or the like by differentially driving ground-engaging elements on opposite vehicle sides
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18145—Cornering
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D11/00—Steering non-deflectable wheels; Steering endless tracks or the like
    • B62D11/20—Endless-track steering having pivoted bogie carrying track
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
    • B62D6/001—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits the torque NOT being among the input parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0008—Feedback, closed loop systems or details of feedback error signal
    • B60W2050/0011—Proportional Integral Differential [PID] controller
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/44—Tracked vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

Abstract

The invention discloses a hydrostatic drive tracked vehicle neural network PID steering control method, which comprises the following steps: s1: reading the target relative steering radius ρ ref of the driver and the target vehicle speed vref adjusted by the steering control coordination control strategy, S2: and judging whether the vehicle of the driver runs straight or not, if so, executing a straight running control subroutine, inputting the result into a neural network PID controller for calculation, and adjusting and sending displacement commands of a pump and a motor, if not, judging that R is 0.5B. After the control method of the invention is added with the self-adaptive adjustment of the neural network, the problem of the adjustment lag of the speed regulation scheme is solved, the steering controller can prejudge the steering state of the vehicle, the target speed of the driver is corrected in advance, and the corrected input signal of the driver can lead the vehicle to be quickly and safely steered under the conditions of no sideslip and no slip and the condition that the system pressure meets the maximum pressure limit.

Description

Hydrostatic drive tracked vehicle neural network PID steering control method
Technical Field
The invention relates to a vehicle steering control method, in particular to a neural network PID steering control method for a hydrostatic drive tracked vehicle, and belongs to the technical field of vehicle steering.
Background
The crawler has the advantages of small grounding pressure, good off-road property, strong climbing and obstacle-crossing capability and the like, and occupies an important position in a future battlefield, and the hydrostatic driving technology is a main development direction of a crawler transmission device in the future due to the large power density, the capability of realizing large-scale stepless speed change and flexible spatial arrangement. Steering ability is an important tactical and technical index, and is particularly important for hydrostatic-driven tracked vehicles. The steering of the hydrostatic driving tracked vehicle is completed by controlling the displacement of the variable displacement pump and the variable displacement motor on the two sides, so that the driving wheels on the two sides of the steering mechanism output different rotating speeds, and the tracks on the two sides are driven to generate speed difference. The steering control technology for the steering mechanism is always a key technology and a difficult technology in the vehicle control technology.
However, in practice, the steering control link of the hydrostatic drive tracked vehicle is complex, the resistances at two sides show problems of wide range change, nonlinearity and the like in the steering process of the vehicle, and in fact, the limitation of the output torque of the hydraulic motor and the output power of the engine exists, so that the problems of slow response speed, unstable steering process, poor track controllability and the like in the steering process tend to be caused. The nonlinear influence factors of the control system are many, and the same research results are not used as reference, so that a novel control method with high practicability and good stability is urgently needed to be provided.
Disclosure of Invention
The invention aims to provide a neural network PID steering control method for a hydrostatic drive tracked vehicle, which aims to solve the problems that the steering has slow response speed, unstable steering process and poor track controllability due to the limitation of the output torque of a hydraulic motor and the output power of an engine in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the method comprises the following steps:
s1: reading a target relative steering radius rhoref of a driver and a target vehicle speed vref which are adjusted by a steering control coordination control strategy;
s2: judging whether the vehicle of the driver is in straight running or not, if so, executing a straight running control subprogram, inputting the result into a neural network PID controller for calculation, and adjusting and sending displacement instructions of a pump and a motor, if not, judging that R is 0.5B;
s3: according to the judgment in the step S2, if R is not equal to 0.5B, under the condition that the system can be ensured not to sideslip and skid, and the system pressure does not exceed the maximum pressure, the speeds v1 and v2 of the crawler belts on the two sides are calculated, the target rotating speeds nM1.ref and nM2.ref of the motors on the inner side and the outer side are further obtained, the rotating speed errors e1 and e2 are obtained according to the target rotating speeds, the rotating speed errors e1 and e2 are input into a neural network PID controller, and the displacement commands of the sending pump and the motors are calculated and output;
s4: according to the judgment in the step S2, when R is equal to 0.5B, v1 is made equal to 0, v2 is calculated, nm1.ref is made equal to 0, the rotation speed error e1 is calculated, e2 is input into the neural network PID controller, and the displacement of the pump and the motor is calculated.
As a preferred technical scheme of the invention, the neural network PID controller adopts a three-layer neural structure network, and an algorithm for calculating the displacement of the sending pump and the motor through the neural network PID controller is as follows:
setting the discharge capacity of a sending pump and a motor as V, wherein the output layer of the neural network controller adopts a sigmoid function, and the discharge capacity is expressed as follows:
Figure BDA0002889647850000021
the input quantities of the output layer are:
x(t)＝kpj(t)epj(t)+kij(t)eij(t)+kdj(t)edj(t) (2)
epj(t)＝nMj.ref(t)-nMj(t) (3)
Figure BDA0002889647850000022
edj(t)＝depj(t)/dt (5)
in the formula, kpj, kij and kdj are respectively proportional, differential and integral coefficients, namely weight coefficients of the neural network; epj, eij, edj are input quantities of the neural network.
As a preferred technical solution of the present invention, a quadratic performance index is introduced in the adjustment of the weight coefficient of the neural network, so that the sum of squares of the output errors is minimized, thereby realizing the optimal control of the adaptive PID, and taking a quadratic performance index function:
Figure BDA0002889647850000031
and (3) correcting the weight coefficient by adopting a gradient descent method:
Figure BDA0002889647850000032
in the formula, η pj, η ij, η dj are learning rates.
As a preferred embodiment of the present invention, the following can be derived from (6) and (12):
Figure BDA0002889647850000033
deducing from the formula (1):
Figure BDA0002889647850000034
as a preferable technical solution of the present invention, the above-mentioned device
Figure BDA0002889647850000035
Substituting formulae (8) and (9) for formula (7) to obtain:
Figure BDA0002889647850000041
according to the formula (10), the neural network controller performs online self-learning training through a feedback network according to epj, eij and edj to enable the motor rotating speed error to approach zero, and online correction weight coefficients kpj, kij and kdj are used for controlling and outputting pump and motor reference displacement on two sides, so that the target relative steering radius and the vehicle speed which are close to the expectation of a driver are obtained finally.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the hydrostatic driving tracked vehicle neural network PID steering control method, after the control method is added with the adaptive adjustment of the neural network, the problem of adjustment lag of a speed regulation scheme is solved, a steering controller can prejudge the steering state of the vehicle, the target speed of a driver is corrected in advance, and the corrected driver input signal can enable the vehicle to be quickly and safely steered under the conditions of no sideslip and no skid and the condition that the system pressure meets the highest pressure limit;
2. the neural network PID steering control method for the hydrostatic drive tracked vehicle is characterized in that a neural network PID weight coefficient initial value is optimized based on a genetic algorithm, an optimized variable pump neural network PID controller weight coefficient initial value and a variable motor neural network PID controller weight coefficient initial value are obtained, overshoot can be effectively inhibited through neural network PID control optimized by the genetic algorithm, the system response is rapid, the target motor rotating speed can be better tracked, and the control effect is better;
3. according to the hydrostatic drive tracked vehicle neural network PID steering control method, the simulation analysis of the uniform speed steering integrated control shows that the hydrostatic drive tracked vehicle can accurately and rapidly steer under the conditions of uniform speed and variable speed of the vehicle.
Drawings
FIG. 1 is a block diagram of a hydrostatic drive tracked vehicle neural network PID steering control of the present invention;
FIG. 2 is a flow chart of the hydrostatic drive tracked vehicle neural network PID steering control of the present invention;
FIG. 3 is a block diagram of the neural network PID architecture of the present invention;
FIG. 4 is a hydrostatic double-side drive transmission structure and control diagram of the tracked vehicle of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, the present invention provides a technical solution of a neural network PID steering control method for a hydrostatic drive tracked vehicle:
as shown in fig. 1-4, the method comprises the following steps:
s1: reading a target relative steering radius rhoref of a driver and a target vehicle speed vref which are adjusted by a steering control coordination control strategy;
s2: judging whether the vehicle of the driver is in straight running or not, if so, executing a straight running control subprogram, inputting the result into a neural network PID controller for calculation, and adjusting and sending displacement instructions of a pump and a motor, if not, judging that R is 0.5B;
s3: according to the judgment in the step S2, if R is not equal to 0.5B, under the condition that the system can be ensured not to sideslip and skid, and the system pressure does not exceed the maximum pressure, the speeds v1 and v2 of the crawler belts on the two sides are calculated, the target rotating speeds nM1.ref and nM2.ref of the motors on the inner side and the outer side are further obtained, the rotating speed errors e1 and e2 are obtained according to the target rotating speeds, the rotating speed errors e1 and e2 are input into a neural network PID controller, and the displacement commands of the sending pump and the motors are calculated and output;
s4: according to the judgment in the step S2, when R is equal to 0.5B, v1 is made equal to 0, v2 is calculated, nm1.ref is made equal to 0, the rotation speed error e1 is calculated, e2 is input into the neural network PID controller, and the displacement of the pump and the motor is calculated.
The neural network PID controller adopts a three-layer neural structure network, and the algorithm for calculating the displacement of the sending pump and the motor through the neural network PID controller is as follows:
setting the discharge capacity of a sending pump and a motor as V, wherein the output layer of the neural network controller adopts a sigmoid function, and the discharge capacity is expressed as follows:
Figure BDA0002889647850000061
the input quantities of the output layer are:
x(t)＝kpj(t)epj(t)+kij(t)eij(t)+kdj(t)edj(t) (2)
epj(t)＝nMj.ref(t)-nMj(t) (3)
Figure BDA0002889647850000062
edj(t)＝depj(t)/dt (5)
in the formula, kpj, kij and kdj are respectively proportional, differential and integral coefficients, namely weight coefficients of the neural network; epj, eij, edj are input quantities of the neural network,
and a quadratic performance index is introduced in the adjustment of the weight coefficient of the neural network, so that the square sum of the output error is minimum, the optimal control of the self-adaptive PID is realized, and a quadratic performance index function is taken as follows:
Figure BDA0002889647850000063
and (3) correcting the weight coefficient by adopting a gradient descent method:
Figure BDA0002889647850000064
in the formula, eta pj, eta ij and eta dj are learning rates,
from (6), 12 it can be deduced:
Figure BDA0002889647850000071
deducing from the formula (1):
Figure BDA0002889647850000072
is provided with
Figure BDA0002889647850000073
Substituting formulae (8) and (9) for formula (7) to obtain:
Figure BDA0002889647850000074
according to the formula (10), the neural network controller performs online self-learning training through a feedback network according to epj, eij and edj to enable the motor rotating speed error to approach zero, and online correction weight coefficients kpj, kij and kdj are used for controlling and outputting pump and motor reference displacement on two sides, so that the target relative steering radius and the vehicle speed which are close to the expectation of a driver are obtained finally.
In the description of the present invention, it is to be understood that the indicated orientations or positional relationships are based on the orientations or positional relationships shown in the drawings and are only for convenience in describing the present invention and simplifying the description, but are not intended to indicate or imply that the indicated devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the present invention.
In the present invention, unless otherwise explicitly specified or limited, for example, it may be fixedly attached, detachably attached, or integrated; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (5)

1. The hydrostatic drive tracked vehicle neural network PID steering control method is characterized by comprising the following steps:
s1: reading a target relative steering radius rhoref of a driver and a target vehicle speed vref which are adjusted by a steering control coordination control strategy;
s2: judging whether the vehicle of the driver is in straight running or not, if so, executing a straight running control subprogram, inputting the result into a neural network PID controller for calculation, and adjusting and sending displacement instructions of a pump and a motor, if not, judging that R is 0.5B;
s3: according to the judgment in the step S2, if R is not equal to 0.5B, under the condition that the system can be ensured not to sideslip and skid, and the system pressure does not exceed the maximum pressure, the speeds v1 and v2 of the crawler belts on the two sides are calculated, the target rotating speeds nM1.ref and nM2.ref of the motors on the inner side and the outer side are further obtained, the rotating speed errors e1 and e2 are obtained according to the target rotating speeds, the rotating speed errors e1 and e2 are input into a neural network PID controller, and the displacement commands of the sending pump and the motors are calculated and output;
s4: according to the judgment in the step S2, when R is equal to 0.5B, v1 is made equal to 0, v2 is calculated, nm1.ref is made equal to 0, the rotation speed error e1 is calculated, e2 is input into the neural network PID controller, and the displacement of the pump and the motor is calculated.
2. The hydrostatic drive tracked vehicle neural network PID steering control method of claim 1, characterized by: the neural network PID controller adopts a three-layer neural structure network, and the algorithm for calculating the displacement of the sending pump and the motor through the neural network PID controller is as follows:
setting the discharge capacity of a sending pump and a motor as V, wherein the output layer of the neural network controller adopts a sigmoid function, and the discharge capacity is expressed as follows:
Figure FDA0002889647840000011
the input quantities of the output layer are:
x(t)＝kpj(t)epj(t)+kij(t)eij(t)+kdj(t)edj(t) (2)
epj(t)＝nMj.ref(t)-nMj(t) (3)
Figure FDA0002889647840000021
edj(t)＝depj(t)/dt (5)
in the formula, kpj, kij and kdj are respectively proportional, differential and integral coefficients, namely weight coefficients of the neural network; epj, eij, edj are input quantities of the neural network.
3. The hydrostatic drive tracked vehicle neural network PID steering control method of claim 2, characterized in that: and introducing a quadratic performance index in the adjustment of the weight coefficient of the neural network to minimize the square sum of output errors, thereby realizing the optimal control of the self-adaptive PID, and taking a quadratic performance index function:
Figure FDA0002889647840000022
and (3) correcting the weight coefficient by adopting a gradient descent method:
Figure FDA0002889647840000023
in the formula, η pj, η ij, η dj are learning rates.
4. The hydrostatic drive tracked vehicle neural network PID steering control method of claim 3, characterized in that: the following can be derived according to (6) and (12):
Figure FDA0002889647840000024
deducing from the formula (1):
Figure FDA0002889647840000031
5. the hydrostatic drive tracked vehicle neural network PID steering control method of claim 4, characterized in that: the device
Figure FDA0002889647840000032
Substituting formulae (8) and (9) for formula (7) to obtain:
Figure FDA0002889647840000033
according to the formula (10), the neural network controller performs online self-learning training through a feedback network according to epj, eij and edj to enable the motor rotating speed error to approach zero, and online correction weight coefficients kpj, kij and kdj are used for controlling and outputting pump and motor reference displacement on two sides, so that the target relative steering radius and the vehicle speed which are close to the expectation of a driver are obtained finally.
CN202110023780.1A 2021-01-08 2021-01-08 Hydrostatic drive tracked vehicle neural network PID steering control method Pending CN112693521A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102245940A (en) * 2008-12-17 2011-11-16 株式会社小松制作所 Control device for hydrostatic transmission vehicle
CN108100034A (en) * 2017-11-07 2018-06-01 北京理工大学 A kind of automatically controlled hydrostatic steering system of split path transmission endless-track vehicle
WO2018142650A1 (en) * 2017-02-02 2018-08-09 日本精工株式会社 Electric power steering apparatus
CN110239638A (en) * 2019-04-30 2019-09-17 长沙桑铼特农业机械设备有限公司 A kind of two track drive tractor proportion expressions steering drive method
CN111152834A (en) * 2020-01-10 2020-05-15 大连理工大学 Electric automobile electronic differential control method based on Ackerman steering correction

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102245940A (en) * 2008-12-17 2011-11-16 株式会社小松制作所 Control device for hydrostatic transmission vehicle
WO2018142650A1 (en) * 2017-02-02 2018-08-09 日本精工株式会社 Electric power steering apparatus
CN108100034A (en) * 2017-11-07 2018-06-01 北京理工大学 A kind of automatically controlled hydrostatic steering system of split path transmission endless-track vehicle
CN110239638A (en) * 2019-04-30 2019-09-17 长沙桑铼特农业机械设备有限公司 A kind of two track drive tractor proportion expressions steering drive method
CN111152834A (en) * 2020-01-10 2020-05-15 大连理工大学 Electric automobile electronic differential control method based on Ackerman steering correction

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